Sentiment Analysis of Game Review in Steam Platform using Random Forest

Authors

  • Mahendra Dwifebri Purbolaksono Telkom University

DOI:

https://doi.org/10.21108/ijoict.v10i2.1007

Keywords:

Sentiment Analysis, Random Forest, TF-IDF, Game Review

Abstract

Steam provides a platform for buyers to write reviews of the software or games they have purchased. Developers will benefit from knowing the criticisms and suggestions given by their community. The number of reviews users give is so large that developers find it difficult to determine whether users like or dislike the games they create. In the Steam application, there is a rating system, but the ratings given by users do not always represent the content of the comments. Therefore, sentiment analysis is used to facilitate developers in understanding the sentiment of the reviews given by users. Sentiment analysis is used to solve this problem. In this research, the sentiment analysis method used is Random Forest with TF-IDF feature extraction in Bigram and Trigram. Based on the research results, scenario testing using Bigram TF-IDF instead of Trigram then in the preprocessing stage without Lemmatization achieved the highest performance. The average F1 score obtained was 62%.

Downloads

Download data is not yet available.

References

[1] Kriz, W. C. (2020). Gaming in the Time of COVID-19. Simulation & Gaming, 51(4), 403-410.
[2] Lin, D., Bezemer, C. P., & Hassan, A. E. (2018). An empirical study of early access games on the Steam platform. Empirical Software Engineering, 23(2), 771-799.
[3] Huang, J. (2018). What can we recommend to game players?-Implementing a system of analyzing game reviews (Master's thesis).
[4] Ahmad, M., Aftab, S., Muhammad, S. S., & Ahmad, S. (2017). Machine learning techniques for sentiment analysis: A review. Int. J. Multidiscip. Sci. Eng, 8(3), 27.
[5] Zuo, Z. (2018). Sentiment analysis of steam review datasets using naive bayes and decision tree classifier.
[6] Hartmann, Jochen, et al. "Comparing automated text classification methods." International Journal of Research in Marketing 36.1 (2019): 20-38.
[7] Ahuja, R., Chug, A., Kohli, S., Gupta, S., & Ahuja, P. (2019). The impact of features extraction on the sentiment analysis. Procedia Computer Science, 152, 341-348.
[8] Febrianta, M. Y., Widiyanesti, S., & Ramadhan, S. R. (2021). Analisis Ulasan Indie Video Game Lokal Pada Steam Menggunakan Sentiment Analysis Dengan Algoritma Naive Bayes Classifier Dan Lda-based Topic Modeling. eProceedings of Management, 8(4).
[9] Tan, J. Y., Chow, A. S. K., & Tan, C. W. (2021, October). Sentiment Analysis on Game Reviews: a Comparative study of Machine Learning Approaches. In International Conference on Digital Transformation and Applications (ICDXA) (Vol. 25, p. 26).
[10] Eberhard, L., Kasper, P., Koncar, P., & Gütl, C. (2018, October). Investigating helpfulness of video game reviews on the steam platform. In 2018 Fifth International Conference on Social Networks Analysis, Management and Security (SNAMS) (pp. 43-50). IEEE.
[11] Khomsah, S. (2021). Sentiment analysis on YouTube comments using word2vec and random forest. Telematika: Jurnal Informatika dan Teknologi Informasi, 18(1), 61-72.
[12] Ramadhan, N. G., Wibowo, M., Mohd Rosely, N. F. L., & Quix, C. (2022). Opinion mining indonesian presidential election on twitter data based on decision tree method. JURNAL INFOTEL, 14(4), 243-248. https://doi.org/10.20895/infotel.v14i4.832
[13] Safruddin, A., Hermawan, A., & Wibowo, A. P. (2020). Implementation of Backpropagation Neural Network in Sentiment Analysis on Twitter To Public Figures. Compiler, 9(2), 101-108.
[14] Purbolaksono, M. D. (2024). Steam Game Review Dataset. Telkom University Dataverse. https://doi.org/10.34820/FK2/9SH7GB
[15] Hakim, A. A., Erwin, A., Eng, K. I., Galinium, M., & Muliady, W. (2014, October). Automated document classification for news article in Bahasa Indonesia based on term frequency inverse document frequency (TF-IDF) approach. In 2014 6th international conference on information technology and electrical engineering (ICITEE) (pp. 1-4). IEEE.
[16] Putri, N. F., Al Faraby, S., & Dwifebri, M. (2021). Analisis Sentimen Pada Produk Kecantikan Dari Ulasan Female Daily Menggunakan Information Gain Dan Svm Classifier. eProceedings of Engineering, 8(5).
[17] Kulkarni, V. Y., & Sinha, P. K. (2012, July). Pruning of random forest classifiers: A survey and future directions. In 2012 International Conference on Data Science & Engineering (ICDSE) (pp. 64-68). IEEE.
[18] Yang, BS., Di, X. & Han, T. (2018). Random forests classifier for machine fault diagnosis. Journal Mechanical Science Technology 22, 1716–1725. https://doi.org/10.1007/s12206-008-0603-6
[19] Ting, K.M. (2011). Confusion Matrix. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-30164-8_157.
[20] Daviran, M., Maghsoudi, A., Ghezelbash, R., & Pradhan, B. (2021). A new strategy for spatial predictive mapping of mineral prospectivity: Automated hyperparameter tuning of random forest approach. Computers & Geosciences, 148, 104688.

Downloads

Published

2024-12-23

How to Cite

Dwifebri Purbolaksono, M. (2024). Sentiment Analysis of Game Review in Steam Platform using Random Forest. International Journal on Information and Communication Technology (IJoICT), 10(2), 10–18. https://doi.org/10.21108/ijoict.v10i2.1007

Issue

Vol. 10 No. 2 (2024): Vol.10 No. 2 Dec 2024

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Manuscript submitted to IJoICT has to be an original work of the author(s), contains no element of plagiarism, and has never been published or is not being considered for publication in other journals. Author(s) shall agree to assign all copyright of published article to IJoICT. Requests related to future re-use and re-publication of major or substantial parts of the article must be consulted with the editors of IJoICT.